Off- Farm Labor Supply of Farm- Families in Rural Georgia
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Transcript of Off- Farm Labor Supply of Farm- Families in Rural Georgia
Off- Farm Labor Supply of Farm- Families in Rural
Georgia
Dr. Ayal Kimhi
Ofir Hoyman
Tbilisi, 2005
Research Goals
Estimating the factors affecting the
labor supply of Georgian family
members off- farm by focusing on:
1. Personal characteristics.
2. Farm characteristics.
3. Having official document owning the
land.
4. Financial risk in farm work.
5. Efficiency in managing the farm.
6. Wage from off- farm- work.
7. Other incomes (not from work).
The Conceptual Model • The family members decide
simultaneously on consumption and
leisure together with farm production
and time allocation to farm and off-
farm for each family member.
• The farm family maximizes utility under
time constraint; budget constraint and
a farm production function.
• The internal solution: each family
member equates his marginal value
of time in farm work, leisure and off-
farm work.
• A member of the farm family will not participate in the local labor market if the wage he could earn is lower than the marginal value of his work on the farm at zero off- farm work hours.
• The assumption is that wage and off-
farm labor supply are endogenous
variables that are determined
simultaneously.
The Empirical Model
• The estimation is a four-step procedure
and based on the Sample Selection
Model.
(A) Estimating the participation equation
and deriving predicted Inverse Mill’s
Ratio;
(B) Estimating the reduced-form labor
supply equation and deriving
predicted off- farm work days;
(C) Estimating the wage equation after
substituting predicted off-farm work
days and deriving predicted wage;
(D) Estimating the structural labor
supply function after substituting
predicted wage.
Data
• The data used were collected by the
Individual Farm Owners’ Survey carried
out on 2003 in four rural districts of
Georgia: Dusheti, Mtskheta, Sagarejo,
and Gardabani.
• The survey included 2,520 individual
farms; 630 farms from each district.
• There are 7,090 individuals in the
sample older than 14.
• 1,577 (22%) individuals are working
off the farm.
Devision of average rural family income by sources
non- farm business8.7%
off- farm work26.5%
farm work43%
public & private transfers21.8%
Distribution of Days Working Off- Farm
0%
5%
10%
15%
20%
25%
30%
30 60 90 120 150 180 210 240 270 300 330 360 More
Work days off- farm
Descriptive Statistics- Averages of Main Variables
Participation Equation Participation Equation
Depend. var.: Depend. var.:
Independ. Var. all obs. males females Independ. Var. all obs. males females
Participation in off- farm work (binary)
0.222 0.275 0.176 Completed technicalcollege (dummy)
0.201 0.191 0.210
Age 44.85 44.37 45.33 Completed/ uncompleted higher education (dummy)
0.197 0.200 0.194
Females (dummy) 0.512 Number of plots 2.562 2.576 2.549Number of childrenup to age 6
0.296 0.282 0.304 Total land size (hectare) 1.679 1.623 1.743
Number of childrenin ages 7- 14
0.511 0.481 0.539 Weighted land quality (1-5) 3.159 3.175 3.151
Number of personsfrom age 15 and up 3.627 3.647 3.601
Technical efficiencyin farm production (0-1) 0.216 0.222 0.214
Number of obs.: 7,090 3,299 3,627 Number of obs.: 7,090 3,299 3,627
work off- farm: yes/ nowork off- farm: yes/ no
Results10% 5% Participation Equation Wage Equation Labor Supply equation
Depend. var.:
Independ. Var. all obs. males females all obs. males females all obs. males females
Predicted work days off- farm
-0.003 -0.003 -0.004
Predicted ln(day wage) -0.651 -0.216 -0.444
Females (dummy) -0.104 -0.431 -0.229
Technical college (dummy) 0.121 0.106 0.138 -0.141 -0.006 -0.278
Higher education (dummy) 0.269 0.222 0.318 -0.121 -0.277 -0.014
Number of children 0-6 0.008 0.035 -0.011 -0.034 -0.046 -0.038
Number of children 7-14 0.013 0.033 -0.002 -0.020 -0.029 -0.005
Number of individuals 15+ 0.003 0.006 -0.0002 0.013 -0.006 0.027
Age 0.027 0.029 0.024 -0.003 -0.003 -0.035 -0.005 -0.004 -0.036
(Age)2 -0.0003(45.4)
-0.0003(44.5)
-0.0003(46.8)
0.0003(58.3)
0.0003(60)
Number of obs.: 7,090 3,299 3,627 1,465 841 594 1,577 907 638
work off- farm: yes/ no ln(w) ln(work days off- farm)
Participation Equation Labor Supply equation
Depend. var.: work off- farm: yes/ no
Independ. Var. all obs. males females all obs. males females
Number of plots -0.018 -0.029 -0.011 0.014 0.003 0.042
Total land size (hectare) 0.00001 -0.001 -0.0003 -0.0001 0.002 -0.001
Weighted land quality (1-5) -0.015 -0.030 -0.011 0.011 0.0002 0.017
Land document (dummy) -0.024 -0.049 0.003 -0.014 0.038 -0.049
Ln(1+public transfers) -0.002 -0.007 0.003 -0.014 0.001 -0.030
Ln(1+private transfers) -0.010 -0.019 -0.003 -0.006 0.006 -0.012
C.V. for production quantities (0-1) -0.050 -0.201 0.015 -0.071 -0.038 0.076C.V. for production prices (0-1) 0.470 0.369 0.332 -1.049 -1.177 -1.146Technical efficiencyin farm production (0-1) -0.229 -0.308 -0.145 0.352 0.479 0.349
Number of obs.: 7,090 3,299 3,627 1,577 907 638
ln(work days off farm)
10% 5%
Conclusions
• Farmers use the off-farm labor
market to supplement farm income.
• Off-farm income compensates
farmers for the income risk they
face in farming.
• The results indicate that off-farm
labor market is in the early stages of
development:
the returns to human capital seem to
be nonexistent relative to the returns
to physical strength.
wages in part-time (temporary or
seasonal) off-farm work surpass the
wages in full-time jobs.
the opportunities for females are
much lower than those for males.
• The off-farm labor decisions are
sensitive to the situation in the land
market:
possession of a land document
decreases off-farm labor
participation, indicating that a land
document increases farmers’
confidence in their ability to make a
living out of farming and therefore
reduce their tendency to seek
alternative income sources.
the farm efficiency has a negative
effect on the probability of working off
the farm, but has a positive effect on
days of work off the farm. This could
indicate that farmers have difficulties
expanding their farming operation.
the difficulties to expand farm
operation can be a consequence of
constraints on land transactions,
credit
rationing, or other constraints.
Thank you
for
listening
Participation Equation Wage Equation Labor Supply equation
Depend. var.:
Independ. Var. all obs. males females all obs. males females all obs. males females
Participation in off- farm work (binary)
0.222 0.275 0.176
Ln(work days off- farm) 5.208 5.202 5.233
Ln(day wage) 1.109 1.321 0.808
Age 44.85 44.37 45.33 43.58 43.48 43.68 43.18 42.99 43.44
Females (dummy) 0.512 0.405 0.405Completed technicalcollege (dummy) 0.201 0.191 0.210 0.260 0.234 0.300
Completed/ uncompleted higher education (dummy)
0.197 0.200 0.194 0.367 0.305 0.457
Number of obs.: 7,090 3,299 3,627 1,465 841 594 1,577 907 638
work off- farm: yes/ no ln(w) ln(work days off- farm)
Participation Equation Labor Supply equation
Depend. var.:
Independ. Var. all obs. males females all obs. males females
Number of childrenup to age 6
0.296 0.282 0.304 0.323 0.344 0.292
Number of childrenin ages 7- 14
0.511 0.481 0.539 0.558 0.564 0.544
Number of personsfrom age 15 and up
3.627 3.647 3.601 3.661 3.660 3.665
Number of plots 2.562 2.576 2.549 2.673 2.622 2.741
Total land size (hectare) 1.679 1.623 1.743 2.261 1.781 2.990
Weighted land quality (1-5) 3.159 3.175 3.151 3.095 3.125 3.057
Technical efficiencyin farm production (0-1) 0.216 0.222 0.214 0.180 0.195 0.162
Number of obs.: 7,090 3,299 3,627 1,577 907 638
work off- farm: yes/ no ln(work days off farm)